# treemind
treemind is designed for analyzing gradient boosting models. It simplifies understanding how features influence predictions within specific intervals and provides powerful tools for analyzing individual features and their interactions.
---
## Algorithm
The `treemind` algorithm analyzes feature contributions and interactions in tree-based models, focusing on specific feature intervals to evaluate their impact on predictions.
**Although the algorithm produces desired results in practice, it lacks formal mathematical proof.**
[Algorithm Overview](https://treemind.readthedocs.io/en/latest/algorithm.html)
### Performance
- Fast and effective for exploratory analysis.
- Highly efficient, even for large datasets.
[Performance Experiments](https://treemind.readthedocs.io/en/latest/experiments/experiment_main.html)
---
## Installation
To install `treemind`, use the following pip command:
```bash
pip install treemind
```
---
## Key Features
1. **Feature Analysis:** Provides statistical analysis on how features behave across different decision splits.
2. **Interaction Analysis:** Identifies complex relationships between features by analyzing how they work together to influence predictions. The algorithm can analyze interactions up to n features, depending on memory constraints and time limitations.
3. **High Performance:** Optimized with Cython for fast execution, even on large models and datasets.
4. **Advanced Visualization:** Offers user-friendly plots to visually explain the model's decision-making process and feature interactions.
5. **Compatibility with Popular Frameworks:** Fully compatible with `xgboost`, `lightgbm` and `catboost`, supporting regression and binary classification tasks.
---
## Usage
This example demonstrates how to set up and use the `Explainer` with a basic `lightgbm` model trained on the Breast Cancer dataset.
For detailed information, please refer to the [API Reference](api_reference.html#api_reference).
### Setup Code
```python
from lightgbm import LGBMClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from treemind import Explainer
from treemind.plot import (
feature_plot,
interaction_plot,
interaction_scatter_plot,
)
# Load the dataset
X, y = load_breast_cancer(return_X_y=True, as_frame=True)
# Train the model
model = LGBMClassifier(verbose=-1)
model.fit(X, y)
```
Once the model is trained, it is ready to be analyzed with the `Explainer`.
### Initializing the `Explainer`
After training the model, initialize the `Explainer` by calling it with the model object:
```python
explainer = Explainer()
explainer(model)
```
### Counting Feature Appearances
The `count_node` function analyzes how often individual features or pairs of features appear in decision splits across the model's trees. This analysis can help identify the most influential features or feature interactions in the model's decision-making process.
To count individual feature appearances in splits:
```python
explainer.count_node(order=1)
```
```none
| column_index | count |
|--------------|-------|
| 21 | 1739 |
| 27 | 1469 |
| 22 | 1422 |
| 23 | 1323 |
| 1 | 1129 |
```
To count feature-pair interactions in splits:
```python
explainer.count_node(order=2)
```
```none
| column1_index | column2_index | count |
|---------------|---------------|-------|
| 21 | 22 | 927 |
| 21 | 23 | 876 |
| 21 | 27 | 852 |
| 1 | 27 | 792 |
| 23 | 27 | 734 |
```
### Analyzing Specific Feature
The `analyze_feature` function calculates statistical metrics for a specific feature based on its split points across the model's trees. This analysis helps in understanding the distribution and impact of a single feature across different split points.
To analyze a specific feature by its index (e.g., 21), use:
```python
feature_df = explainer.analyze_feature(21)
```
```none
| worst_texture_lb | Worst_texture_ub | value | std | count |
|------------------|------------------|-----------|----------|---------|
| -inf | 18.460 | 3.185128 | 8.479232 | 402.24 |
| 18.460 | 19.300 | 3.160656 | 8.519873 | 402.39 |
| 19.300 | 19.415 | 3.119814 | 8.489262 | 401.85 |
| 19.415 | 20.225 | 3.101601 | 8.490439 | 402.55 |
| 20.225 | 20.360 | 2.772929 | 8.711773 | 433.16 |
```
To visualize feature statistics calculated by `analyze_feature` using `feature_plot`:
```python
feature_plot(feature_df)
```
### Analyzing Feature Interactions
The `analyze_feature` function given multiple indices calculates the dependency between two or more features by examining their split points across the model’s trees.
To analyze an interaction between two features (e.g., feature indices 21 and 22), use:
```python
df = explainer.analyze_feature([21, 22])
```
Example output:
```none
| worst_texture_lb | worst_texture_ub | worst_concave_points_lb | worst_concave_points_ub | value | std | count |
|------------------|------------------|--------------------------|------------------------|-----------|----------|---------|
| -inf | 18.46 | -inf | 0.058860 | 4.929324 | 7.679424 | 355.40 |
| -inf | 18.46 | 0.058860 | 0.059630 | 4.928594 | 7.679772 | 355.34 |
| -inf | 18.46 | 0.059630 | 0.065540 | 4.923128 | 7.679783 | 355.03 |
| -inf | 18.46 | 0.065540 | 0.069320 | 4.912888 | 7.682064 | 354.70 |
| -inf | 18.46 | 0.069320 | 0.069775 | 4.912888 | 7.682064 | 354.70 |
```
To visualize interactions between two features calculated by `analyze_interaction` using `interaction_plot`:
```python
interaction_plot(df)
```
To visualize interactions between two features on given data by `analyze_interaction` using `interaction_scatter_plot`:
```python
interaction_scatter_plot(X, df, 21, 22)
```
---
## Contributing
Contributions are welcome! If you'd like to improve `treemind` or suggest new features, feel free to fork the repository and submit a pull request.
## License
`treemind` is released under the BSD 3-Clause License. See the LICENSE file for more details.
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"description": "# treemind \ntreemind is designed for analyzing gradient boosting models. It simplifies understanding how features influence predictions within specific intervals and provides powerful tools for analyzing individual features and their interactions.\n\n---\n## Algorithm\n\nThe `treemind` algorithm analyzes feature contributions and interactions in tree-based models, focusing on specific feature intervals to evaluate their impact on predictions.\n\n**Although the algorithm produces desired results in practice, it lacks formal mathematical proof.**\n\n[Algorithm Overview](https://treemind.readthedocs.io/en/latest/algorithm.html)\n\n### Performance\n- Fast and effective for exploratory analysis.\n- Highly efficient, even for large datasets.\n\n[Performance Experiments](https://treemind.readthedocs.io/en/latest/experiments/experiment_main.html)\n\n---\n\n## Installation\nTo install `treemind`, use the following pip command:\n\n```bash\npip install treemind\n```\n---\n## Key Features\n\n1. **Feature Analysis:** Provides statistical analysis on how features behave across different decision splits.\n\n2. **Interaction Analysis:** Identifies complex relationships between features by analyzing how they work together to influence predictions. The algorithm can analyze interactions up to n features, depending on memory constraints and time limitations.\n\n3. **High Performance:** Optimized with Cython for fast execution, even on large models and datasets.\n\n4. **Advanced Visualization:** Offers user-friendly plots to visually explain the model's decision-making process and feature interactions. \n\n5. **Compatibility with Popular Frameworks:** Fully compatible with `xgboost`, `lightgbm` and `catboost`, supporting regression and binary classification tasks.\n---\n\n## Usage\n\nThis example demonstrates how to set up and use the `Explainer` with a basic `lightgbm` model trained on the Breast Cancer dataset. \n\nFor detailed information, please refer to the [API Reference](api_reference.html#api_reference).\n\n### Setup Code\n\n```python\nfrom lightgbm import LGBMClassifier\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split\n\nfrom treemind import Explainer\nfrom treemind.plot import (\n feature_plot,\n interaction_plot,\n interaction_scatter_plot,\n)\n\n# Load the dataset\nX, y = load_breast_cancer(return_X_y=True, as_frame=True)\n\n# Train the model\nmodel = LGBMClassifier(verbose=-1)\nmodel.fit(X, y)\n```\n\nOnce the model is trained, it is ready to be analyzed with the `Explainer`.\n\n### Initializing the `Explainer`\n\nAfter training the model, initialize the `Explainer` by calling it with the model object:\n\n```python\nexplainer = Explainer()\nexplainer(model)\n```\n\n### Counting Feature Appearances\n\nThe `count_node` function analyzes how often individual features or pairs of features appear in decision splits across the model's trees. This analysis can help identify the most influential features or feature interactions in the model's decision-making process.\n\nTo count individual feature appearances in splits:\n\n```python\nexplainer.count_node(order=1)\n```\n\n```none\n| column_index | count |\n|--------------|-------|\n| 21 | 1739 |\n| 27 | 1469 |\n| 22 | 1422 |\n| 23 | 1323 |\n| 1 | 1129 |\n```\n\nTo count feature-pair interactions in splits:\n\n```python\nexplainer.count_node(order=2)\n```\n\n```none\n| column1_index | column2_index | count |\n|---------------|---------------|-------|\n| 21 | 22 | 927 |\n| 21 | 23 | 876 |\n| 21 | 27 | 852 |\n| 1 | 27 | 792 |\n| 23 | 27 | 734 |\n```\n\n### Analyzing Specific Feature\n\nThe `analyze_feature` function calculates statistical metrics for a specific feature based on its split points across the model's trees. This analysis helps in understanding the distribution and impact of a single feature across different split points.\n\nTo analyze a specific feature by its index (e.g., 21), use:\n\n```python\nfeature_df = explainer.analyze_feature(21)\n```\n\n```none\n| worst_texture_lb | Worst_texture_ub | value | std | count |\n|------------------|------------------|-----------|----------|---------|\n| -inf | 18.460 | 3.185128 | 8.479232 | 402.24 |\n| 18.460 | 19.300 | 3.160656 | 8.519873 | 402.39 |\n| 19.300 | 19.415 | 3.119814 | 8.489262 | 401.85 |\n| 19.415 | 20.225 | 3.101601 | 8.490439 | 402.55 |\n| 20.225 | 20.360 | 2.772929 | 8.711773 | 433.16 |\n```\n\nTo visualize feature statistics calculated by `analyze_feature` using `feature_plot`:\n\n```python\nfeature_plot(feature_df)\n```\n\n\n\n### Analyzing Feature Interactions\n\nThe `analyze_feature` function given multiple indices calculates the dependency between two or more features by examining their split points across the model\u2019s trees.\n\nTo analyze an interaction between two features (e.g., feature indices 21 and 22), use:\n\n```python\ndf = explainer.analyze_feature([21, 22])\n```\n\nExample output:\n\n```none\n| worst_texture_lb | worst_texture_ub | worst_concave_points_lb | worst_concave_points_ub | value | std | count |\n|------------------|------------------|--------------------------|------------------------|-----------|----------|---------|\n| -inf | 18.46 | -inf | 0.058860 | 4.929324 | 7.679424 | 355.40 |\n| -inf | 18.46 | 0.058860 | 0.059630 | 4.928594 | 7.679772 | 355.34 |\n| -inf | 18.46 | 0.059630 | 0.065540 | 4.923128 | 7.679783 | 355.03 |\n| -inf | 18.46 | 0.065540 | 0.069320 | 4.912888 | 7.682064 | 354.70 |\n| -inf | 18.46 | 0.069320 | 0.069775 | 4.912888 | 7.682064 | 354.70 |\n\n```\n\nTo visualize interactions between two features calculated by `analyze_interaction` using `interaction_plot`:\n\n```python\ninteraction_plot(df)\n```\n\n\n\nTo visualize interactions between two features on given data by `analyze_interaction` using `interaction_scatter_plot`:\n\n```python\ninteraction_scatter_plot(X, df, 21, 22)\n```\n\n\n\n\n---\n\n## Contributing\nContributions are welcome! If you'd like to improve `treemind` or suggest new features, feel free to fork the repository and submit a pull request.\n\n## License\n`treemind` is released under the BSD 3-Clause License. See the LICENSE file for more details.\n",
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